private void LoadHandTrainingPatternsFromDir(string path)
{
try
{
byte[] TrainPatterns;
MNistHeight = 32;
MNistWidth = 32;
MNistSize = MNistWidth * MNistHeight;
int TrainingLabelCount = 10;
int LabelImageCount = 20;
TrainingPatternsCount = TrainingLabelCount*LabelImageCount;
TrainPatterns = new byte[TrainingPatternsCount * MNistSize];
unsafe
{
for (int ii = 0; ii < TrainingLabelCount; ii++)
{
string type = ii.ToString("D1");
//Image<Bgr, Byte> image = new Image<Bgr, byte>(path + "\\" + type + ".jpg").Resize(32, 32, Emgu.CV.CvEnum.INTER.CV_INTER_AREA); //Read the files as an 8-bit Bgr image
//Image<Gray, Byte> gray = image.Convert<Gray, Byte>(); //Convert it to Grayscale
Capture cap = new Capture(path + "\\" + type + ".MOV");
for (int i = 0; i < LabelImageCount; i++)
{
Image<Gray, Byte> gray = cap.QueryGrayFrame().Resize(32, 32, Emgu.CV.CvEnum.INTER.CV_INTER_AREA);
for (int j = 0; j < MNistSize; j++)
{
TrainPatterns[ii * MNistSize * LabelImageCount + i * MNistSize + j] = ((byte*)gray.MIplImage.imageData + j)[0];
}
}
cap.Dispose();
}
}
MNISTTraining = new ByteImageData[TrainingPatternsCount];
Parallel.For(0, TrainingPatternsCount, parallelOption, j =>
{
int label = j / LabelImageCount;
ByteImageData imageData = new ByteImageData(label, new byte[MNistSize]);
for (int i = 0; i < MNistSize; i++)
{
imageData.Image[i] = TrainPatterns[(j * MNistSize) + i];
}
MNISTTraining[j] = imageData;
});
}
catch (Exception)
{
throw;
}
}
private void LoadTestingPatternsFromFile(string path)
{
try
{
byte[] TestPatterns;
using (FileStream fileStreamTestPatterns = System.IO.File.Open(path + @"\t10k-images-idx3-ubyte", FileMode.Open, FileAccess.Read, FileShare.Read))
{
byte[] magicNumber = new byte[16];
fileStreamTestPatterns.Read(magicNumber, 0, 16);
//DataType dataType = (DataType)magicNumber[2];
//int dimensions = (int)magicNumber[3];
TestingPatternsCount = magicNumber[7] + (magicNumber[6] * 256) + (magicNumber[5] * 65536) + (magicNumber[4] * 16777216);
MNistHeight = magicNumber[11] + (magicNumber[10] * 256) + (magicNumber[9] * 65536) + (magicNumber[8] * 16777216);
MNistWidth = magicNumber[15] + (magicNumber[14] * 256) + (magicNumber[13] * 65536) + (magicNumber[12] * 16777216);
MNistSize = MNistWidth * MNistHeight;
TestPatterns = new byte[TestingPatternsCount * MNistSize];
fileStreamTestPatterns.Seek(16, SeekOrigin.Begin);
fileStreamTestPatterns.Read(TestPatterns, 0, TestingPatternsCount * MNistSize);
}
byte[] TestLabels;
using (FileStream fileStreamTestLabels = System.IO.File.Open(path + @"\t10k-labels-idx1-ubyte", FileMode.Open, FileAccess.Read, FileShare.Read))
{
TestLabels = new byte[TestingPatternsCount];
fileStreamTestLabels.Seek(8, SeekOrigin.Begin);
fileStreamTestLabels.Read(TestLabels, 0, TestingPatternsCount);
}
MNISTTesting = new ByteImageData[TestingPatternsCount];
Parallel.For(0, TestingPatternsCount, parallelOption, j =>
{
ByteImageData pattern = new ByteImageData(TestLabels[j], new byte[MNistSize]);
for (int i = 0; i < MNistSize; i++)
{
pattern.Image[i] = TestPatterns[(j* MNistSize ) + i];
}
MNISTTesting[j] = pattern;
});
}
catch (Exception)
{
throw;
}
}
private void LoadHandTestingPatternsFromDir(string path)
{
try
{
byte[] TestPatterns;
MNistHeight = 32;
MNistWidth = 32;
MNistSize = MNistWidth * MNistHeight;
int TrainingLabelCount = 9;
int LabelImageCount = 100;
TestingPatternsCount = TrainingLabelCount * LabelImageCount;
TestPatterns = new byte[TestingPatternsCount * MNistSize];
//Capture cap = new Capture(@"D:\ebooks\hand gestrue recognition\hand data set\mov\0.MOV");
unsafe
{
for (int ii = 0; ii < TrainingLabelCount; ii++)
{
string type = ii.ToString("D1");
//Image<Bgr, Byte> image = new Image<Bgr, byte>(path + "\\" + type + ".jpg").Resize(32, 32, Emgu.CV.CvEnum.INTER.CV_INTER_AREA); //Read the files as an 8-bit Bgr image
//Image<Gray, Byte> gray = image.Convert<Gray, Byte>(); //Convert it to Grayscale
Capture cap = new Capture(path + "\\" + type + ".MOV");
for(int i =0; i<200;i++)
{
cap.QueryGrayFrame();//skip first 200 frames
}
for (int i = 0; i < LabelImageCount; i++)
{
Image<Gray, Byte> gray = cap.QueryGrayFrame().Resize(32, 32, Emgu.CV.CvEnum.INTER.CV_INTER_AREA);
for (int j = 0; j < MNistSize; j++)
{
TestPatterns[ii * MNistSize * LabelImageCount + i * MNistSize + j] = ((byte*)gray.MIplImage.imageData + j)[0];
}
}
cap.Dispose();
}
}
MNISTTesting = new ByteImageData[TestingPatternsCount];
Parallel.For(0, TestingPatternsCount, parallelOption, j =>
{
ByteImageData pattern = new ByteImageData(j / LabelImageCount, new byte[MNistSize]);
for (int i = 0; i < MNistSize; i++)
{
pattern.Image[i] = TestPatterns[(j * MNistSize) + i];
}
MNISTTesting[j] = pattern;
});
}
catch (Exception)
{
throw;
}
}